"""
Unified configuration for Hugging Face datasets integration.
All runner modules should import from this module instead of defining their own paths.
"""

import os
import json
from pathlib import Path
from typing import Any, Dict, Optional, List, Tuple

# Try to import required libraries
try:
    from datasets import load_dataset
    DATASETS_AVAILABLE = True
except ImportError:
    print("⚠️  datasets library not available - HF dataset loading disabled")
    DATASETS_AVAILABLE = False

try:
    from huggingface_hub import hf_hub_download
    HF_HUB_AVAILABLE = True
except ImportError:
    print("⚠️  huggingface_hub library not available - HF file loading disabled")
    HF_HUB_AVAILABLE = False

# Environment variables for dataset names
ARTEFACT_JSON_DATASET = os.getenv('ARTEFACT_JSON_DATASET', 'samwaugh/artefact-json')
ARTEFACT_EMBEDDINGS_DATASET = os.getenv('ARTEFACT_EMBEDDINGS_DATASET', 'samwaugh/artefact-embeddings')
ARTEFACT_MARKDOWN_DATASET = os.getenv('ARTEFACT_MARKDOWN_DATASET', 'samwaugh/artefact-markdown')

# Legacy path variables for backward compatibility
JSON_INFO_DIR = "/data/hub/datasets--samwaugh--artefact-json/snapshots/latest"
EMBEDDINGS_DIR = "/data/hub/datasets--samwaugh--artefact-embeddings/snapshots/latest"
MARKDOWN_DIR = "/data/hub/datasets--samwaugh--artefact-markdown/snapshots/latest"

# Embedding file paths for backward compatibility
CLIP_EMBEDDINGS_ST = Path(EMBEDDINGS_DIR) / "clip_embeddings.safetensors"
PAINTINGCLIP_EMBEDDINGS_ST = Path(EMBEDDINGS_DIR) / "paintingclip_embeddings.safetensors"
CLIP_SENTENCE_IDS = Path(EMBEDDINGS_DIR) / "clip_embeddings_sentence_ids.json"
PAINTINGCLIP_SENTENCE_IDS = Path(EMBEDDINGS_DIR) / "paintingclip_embeddings_sentence_ids.json"
CLIP_EMBEDDINGS_DIR = EMBEDDINGS_DIR
PAINTINGCLIP_EMBEDDINGS_DIR = EMBEDDINGS_DIR

# READ root (repo data - read-only)
PROJECT_ROOT = Path(__file__).resolve().parents[2]
DATA_READ_ROOT = PROJECT_ROOT / "data"

# WRITE root (Space volume - writable)
# HF Spaces uses /data for persistent storage
WRITE_ROOT = Path(os.getenv("HF_HOME", "/data"))

# Check if the directory exists and is writable
if not WRITE_ROOT.exists():
    print(f"⚠️  WRITE_ROOT {WRITE_ROOT} does not exist, trying to create it")
    try:
        WRITE_ROOT.mkdir(parents=True, exist_ok=True)
        print(f"✅ Created WRITE_ROOT: {WRITE_ROOT}")
    except Exception as e:
        print(f"⚠️  Failed to create {WRITE_ROOT}: {e}")
        print(f"⚠️  This may be expected in local development - continuing anyway")
        # Don't raise an error, just continue

# Check write permissions (only if directory exists)
if WRITE_ROOT.exists() and not os.access(WRITE_ROOT, os.W_OK):
    print(f"❌ WRITE_ROOT {WRITE_ROOT} is not writable")
    print(f"❌ Current permissions: {oct(WRITE_ROOT.stat().st_mode)[-3:]}")
    print(f"❌ Owner: {WRITE_ROOT.owner()}")
    print(f"⚠️  This may be expected in local development - continuing anyway")
    # Don't raise an error, just continue

print(f"✅ Using WRITE_ROOT: {WRITE_ROOT}")
print(f"✅ Using READ_ROOT: {DATA_READ_ROOT}")

# Read-only directories (from repo)
MODELS_DIR = DATA_READ_ROOT / "models"
MARKER_DIR = DATA_READ_ROOT / "marker_output"

# Model directories
PAINTINGCLIP_MODEL_DIR = MODELS_DIR / "PaintingClip"  # Note the capital C

# Writable directories (outside repo)
OUTPUTS_DIR = WRITE_ROOT / "outputs"
ARTIFACTS_DIR = WRITE_ROOT / "artifacts"

# Ensure writable directories exist
for dir_path in [OUTPUTS_DIR, ARTIFACTS_DIR]:
    try:
        dir_path.mkdir(parents=True, exist_ok=True)
        print(f"✅ Ensured directory exists: {dir_path}")
    except Exception as e:
        print(f"⚠️  Could not create directory {dir_path}: {e}")

# Global data variables (will be populated from HF datasets)
sentences: Dict[str, Any] = {}
works: Dict[str, Any] = {}
creators: Dict[str, Any] = {}
topics: Dict[str, Any] = {}
topic_names: Dict[str, Any] = {}

def load_json_from_hf(repo_id: str, filename: str) -> Optional[Dict[str, Any]]:
    """Load a single JSON file from Hugging Face repository"""
    if not HF_HUB_AVAILABLE:
        print(f"⚠️  huggingface_hub not available - cannot load {filename}")
        return None
        
    try:
        print(f"🔍 Downloading {filename} from {repo_id}...")
        file_path = hf_hub_download(
            repo_id=repo_id, 
            filename=filename, 
            repo_type="dataset"
        )
        
        with open(file_path, 'r', encoding='utf-8') as f:
            data = json.load(f)
        
        print(f"✅ Successfully loaded {filename}: {len(data)} entries")
        return data
    except Exception as e:
        print(f"❌ Failed to load {filename} from {repo_id}: {e}")
        return None

def load_json_datasets() -> Optional[Dict[str, Any]]:
    """Load all JSON datasets from Hugging Face"""
    if not HF_HUB_AVAILABLE:
        print("⚠️  huggingface_hub library not available - skipping HF dataset loading")
        return None
        
    try:
        print("📥 Loading JSON files from Hugging Face repository...")
        
        # Load individual JSON files
        global sentences, works, creators, topics, topic_names
        
        creators = load_json_from_hf(ARTEFACT_JSON_DATASET, 'creators.json') or {}
        sentences = load_json_from_hf(ARTEFACT_JSON_DATASET, 'sentences.json') or {}
        works = load_json_from_hf(ARTEFACT_JSON_DATASET, 'works.json') or {}
        topics = load_json_from_hf(ARTEFACT_JSON_DATASET, 'topics.json') or {}
        topic_names = load_json_from_hf(ARTEFACT_JSON_DATASET, 'topic_names.json') or {}
        
        print(f"✅ Successfully loaded JSON files from HF:")
        print(f"   Sentences: {len(sentences)} entries")
        print(f"   Works: {len(works)} entries")
        print(f"   Creators: {len(creators)} entries")
        print(f"   Topics: {len(topics)} entries")
        print(f"   Topic Names: {len(topic_names)} entries")
        
        return {
            'creators': creators,
            'sentences': sentences,
            'works': works,
            'topics': topics,
            'topic_names': topic_names
        }
    except Exception as e:
        print(f"❌ Failed to load JSON datasets from HF: {e}")
        return None

def load_embeddings_datasets() -> Optional[Dict[str, Any]]:
    """Load embeddings datasets from Hugging Face using direct file download"""
    if not HF_HUB_AVAILABLE:
        print("⚠️  huggingface_hub library not available - skipping HF embeddings loading")
        return None
        
    try:
        print(f"📥 Loading embeddings from {ARTEFACT_EMBEDDINGS_DATASET}...")
        
        # Return a flag indicating we should use direct file download
        # The actual loading will be done in inference.py
        return {
            'use_direct_download': True,
            'repo_id': ARTEFACT_EMBEDDINGS_DATASET
        }
    except Exception as e:
        print(f"❌ Failed to load embeddings datasets from HF: {e}")
        return None

# Global variable to cache the markdown directory
_markdown_dir_cache = None

def clear_markdown_cache() -> bool:
    """Clear the markdown cache to force a fresh download"""
    try:
        import shutil
        markdown_cache_dir = WRITE_ROOT / "markdown_cache"
        if markdown_cache_dir.exists():
            print(f"🗑️  Clearing markdown cache at {markdown_cache_dir}")
            shutil.rmtree(markdown_cache_dir)
            print(f"✅ Markdown cache cleared successfully")
            return True
        else:
            print(f"ℹ️  No markdown cache found to clear")
            return True
    except Exception as e:
        print(f"❌ Failed to clear markdown cache: {e}")
        return False

def get_markdown_cache_info() -> dict:
    """Get information about the current markdown cache"""
    try:
        import shutil
        markdown_cache_dir = WRITE_ROOT / "markdown_cache"
        works_dir = markdown_cache_dir / "works"
        
        if not works_dir.exists():
            return {
                "exists": False,
                "size_gb": 0,
                "work_count": 0,
                "file_count": 0
            }
        
        # Calculate total size
        total_size = sum(f.stat().st_size for f in works_dir.rglob('*') if f.is_file())
        size_gb = total_size / (1024**3)
        
        # Count files and directories
        file_count = len(list(works_dir.rglob('*')))
        work_count = len([d for d in works_dir.iterdir() if d.is_dir()])
        
        return {
            "exists": True,
            "size_gb": round(size_gb, 2),
            "work_count": work_count,
            "file_count": file_count,
            "path": str(works_dir)
        }
    except Exception as e:
        print(f"❌ Failed to get cache info: {e}")
        return {"exists": False, "error": str(e)}

def load_markdown_dataset(force_refresh: bool = False) -> Optional[Path]:
    """Load markdown dataset from Hugging Face and return the local path"""
    if not HF_HUB_AVAILABLE:
        print("⚠️  huggingface_hub not available - cannot load markdown dataset")
        return None
        
    try:
        print(f"📥 Loading markdown dataset from {ARTEFACT_MARKDOWN_DATASET}...")
        
        # Create a local cache directory for the markdown dataset
        markdown_cache_dir = WRITE_ROOT / "markdown_cache"
        markdown_cache_dir.mkdir(parents=True, exist_ok=True)
        
        works_dir = markdown_cache_dir / "works"
        
        # Check if we should force refresh or if cache is incomplete
        if force_refresh:
            print("🔄 Force refresh requested - clearing cache")
            clear_markdown_cache()
        else:
            # Check cache completeness
            cache_info = get_markdown_cache_info()
            if cache_info["exists"]:
                print(f"📊 Cache info: {cache_info['work_count']} works, {cache_info['size_gb']}GB")
                
                # If we have significantly fewer works than expected, clear and re-download
                expected_works = 7200  # Based on your dataset
                if cache_info["work_count"] < expected_works * 0.8:  # Less than 80% of expected
                    print(f"⚠️  Cache incomplete ({cache_info['work_count']}/{expected_works} works) - clearing and re-downloading")
                    clear_markdown_cache()
                else:
                    print(f"✅ Using cached markdown dataset at {works_dir}")
                    # Even if markdown folders exist, images may be missing. Perform a
                    # lightweight sampling check and, if needed, resume image downloads.
                    try:
                        if _images_likely_missing(works_dir):
                            print("🖼️  Images appear to be missing or incomplete – resuming image download phase...")
                            _download_images_phase_only(works_dir)
                        else:
                            print("🖼️  Images appear present for sampled works – skipping image download phase")
                    except Exception as e:
                        print(f"⚠️  Image presence check failed: {e}")
                    return works_dir
        
        # Use optimized download approach
        print("📥 Downloading markdown dataset with optimized approach...")
        return _download_markdown_optimized(works_dir)
            
    except Exception as e:
        print(f"❌ Failed to load markdown dataset: {e}")
        return None

def _download_markdown_optimized(works_dir: Path) -> Optional[Path]:
    """Robust markdown dataset download with error handling and progress persistence"""
    try:
        from huggingface_hub import list_repo_files
        import concurrent.futures
        import threading
        import time
        import json
        
        # Create progress tracking file
        progress_file = works_dir.parent / "download_progress.json"
        
        # Load existing progress if available
        progress = {"markdown_completed": set(), "image_batches_completed": set()}
        if progress_file.exists():
            try:
                with open(progress_file, 'r') as f:
                    saved_progress = json.load(f)
                    progress["markdown_completed"] = set(saved_progress.get("markdown_completed", []))
                    progress["image_batches_completed"] = set(saved_progress.get("image_batches_completed", []))
                print(f"📊 Resuming download from previous progress...")
            except Exception as e:
                print(f"⚠️  Could not load progress file: {e}")
        
        # Get the list of files in the dataset
        print("🔍 Discovering files in dataset...")
        files = list_repo_files(repo_id=ARTEFACT_MARKDOWN_DATASET, repo_type="dataset")
        
        # Filter for work directories
        work_dirs = set()
        for file_path in files:
            if file_path.startswith("works/"):
                parts = file_path.split("/")
                if len(parts) >= 2:
                    work_id = parts[1]
                    if work_id.startswith("W"):  # Only include work IDs
                        work_dirs.add(work_id)
        
        print(f"📊 Found {len(work_dirs)} work directories to download")
        
        # Phase 1: Download only markdown files (fast)
        print("📄 Phase 1: Downloading markdown files only...")
        _download_markdown_files_robust(works_dir, work_dirs, files, progress, progress_file)
        
        # Phase 2: Download images in smaller batches (more resilient)
        print("🖼️  Phase 2: Downloading images in smaller batches...")
        _download_images_robust(works_dir, work_dirs, files, progress, progress_file)
        
        # Clean up progress file on success
        if progress_file.exists():
            progress_file.unlink()
        
        print(f"✅ Successfully downloaded markdown dataset to {works_dir}")
        return works_dir
        
    except Exception as e:
        print(f"❌ Optimized download failed: {e}")
        import traceback
        traceback.print_exc()
        return None

def _images_likely_missing(works_dir: Path, sample_size: int = 20) -> bool:
    """Quickly assess whether images are present in the cache.

    We sample up to `sample_size` work directories and check for any .jpg/.png
    files either under <work>/images/ or directly inside <work>/.

    Returns True if fewer than 20% of sampled works have at least one image.
    """
    try:
        work_dirs = [d for d in works_dir.iterdir() if d.is_dir()]
        if not work_dirs:
            print("🖼️  Image check: no work directories found – treating as missing")
            return True

        sampled = work_dirs[:sample_size]
        has_images_count = 0
        for work_dir in sampled:
            images_dir = work_dir / "images"
            found = False
            if images_dir.exists():
                if any(images_dir.glob("*.jpg")) or any(images_dir.glob("*.jpeg")) or any(images_dir.glob("*.png")):
                    found = True
            # Fallback: look in the work dir directly
            if not found:
                if any(work_dir.glob("*.jpg")) or any(work_dir.glob("*.jpeg")) or any(work_dir.glob("*.png")):
                    found = True
            if found:
                has_images_count += 1

        ratio = has_images_count / max(1, len(sampled))
        print(f"🖼️  Image check: {has_images_count}/{len(sampled)} sampled works have images (ratio={ratio:.2f})")
        return ratio < 0.2
    except Exception as e:
        print(f"⚠️  Image sampling check failed: {e}")
        # Be conservative – assume images are missing so we attempt to download them
        return True

def _download_images_phase_only(works_dir: Path) -> Optional[Path]:
    """Resume/perform only the image download phase without touching markdown files.

    This function discovers files on the HF repo, constructs the list of works,
    loads any existing download progress, and runs the robust image downloader.
    """
    try:
        from huggingface_hub import list_repo_files
        import json

        progress_file = works_dir.parent / "download_progress.json"

        # Load existing progress if available
        progress = {"markdown_completed": set(), "image_batches_completed": set()}
        if progress_file.exists():
            try:
                with open(progress_file, 'r') as f:
                    saved_progress = json.load(f)
                    progress["markdown_completed"] = set(saved_progress.get("markdown_completed", []))
                    progress["image_batches_completed"] = set(saved_progress.get("image_batches_completed", []))
                print(f"📊 Resuming image download from previous progress...")
            except Exception as e:
                print(f"⚠️  Could not load progress file: {e}")

        print("🔍 Discovering files in dataset (images phase only)...")
        files = list_repo_files(repo_id=ARTEFACT_MARKDOWN_DATASET, repo_type="dataset")

        work_dirs = set()
        for file_path in files:
            if file_path.startswith("works/"):
                parts = file_path.split("/")
                if len(parts) >= 2:
                    work_id = parts[1]
                    if work_id.startswith("W"):
                        work_dirs.add(work_id)

        print(f"📊 Images phase: {len(work_dirs)} work directories discovered")
        _download_images_robust(works_dir, work_dirs, files, progress, progress_file)
        return works_dir
    except Exception as e:
        print(f"❌ Images-phase-only download failed: {e}")
        import traceback
        traceback.print_exc()
        return None

def _download_markdown_files_robust(works_dir: Path, work_dirs: set, files: list, progress: dict, progress_file: Path) -> None:
    """Download markdown files with retry logic and progress persistence"""
    import concurrent.futures
    import threading
    import time
    import json
    from requests.exceptions import ReadTimeout, ConnectionError, HTTPError
    
    def download_markdown_file_with_retry(work_id: str, max_retries: int = 3) -> bool:
        """Download a single markdown file with retry logic"""
        for attempt in range(max_retries):
            try:
                work_dir = works_dir / work_id
                work_dir.mkdir(parents=True, exist_ok=True)
                
                # Check if already downloaded
                if (work_dir / f"{work_id}.md").exists():
                    return True
                
                md_file = hf_hub_download(
                    repo_id=ARTEFACT_MARKDOWN_DATASET,
                    filename=f"works/{work_id}/{work_id}.md",
                    repo_type="dataset"
                )
                
                import shutil
                shutil.copy2(md_file, work_dir / f"{work_id}.md")
                return True
                
            except (ReadTimeout, ConnectionError, HTTPError) as e:
                if attempt < max_retries - 1:
                    wait_time = 2 ** attempt  # Exponential backoff
                    print(f"⚠️  Retry {attempt + 1}/{max_retries} for {work_id} after {wait_time}s (error: {e})")
                    time.sleep(wait_time)
                else:
                    print(f"❌ Failed to download markdown for {work_id} after {max_retries} attempts: {e}")
                    return False
            except Exception as e:
                print(f"❌ Unexpected error downloading {work_id}: {e}")
                return False
        
        return False
    
    def save_progress():
        """Save current progress to file"""
        try:
            progress_data = {
                "markdown_completed": list(progress["markdown_completed"]),
                "image_batches_completed": list(progress["image_batches_completed"])
            }
            with open(progress_file, 'w') as f:
                json.dump(progress_data, f)
        except Exception as e:
            print(f"⚠️  Could not save progress: {e}")
    
    # Filter out already completed works
    remaining_works = [w for w in work_dirs if w not in progress["markdown_completed"]]
    
    if not remaining_works:
        print("📄 All markdown files already downloaded")
        return
    
    print(f"📄 Downloading {len(remaining_works)} markdown files...")
    completed = len(progress["markdown_completed"])
    failed = 0
    
    # Use even fewer workers to be more gentle
    with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
        future_to_work = {executor.submit(download_markdown_file_with_retry, work_id): work_id for work_id in remaining_works}
        
        for future in concurrent.futures.as_completed(future_to_work):
            work_id = future_to_work[future]
            try:
                success = future.result()
                if success:
                    progress["markdown_completed"].add(work_id)
                    completed += 1
                else:
                    failed += 1
                
                # Save progress every 100 files
                if (completed + failed) % 100 == 0:
                    save_progress()
                    print(f"📄 Progress: {completed}/{len(work_dirs)} markdown files (failed: {failed})")
                    # Longer delay to be more gentle
                    time.sleep(3)
                    
            except Exception as e:
                print(f"❌ Error processing {work_id}: {e}")
                failed += 1
    
    # Final progress save
    save_progress()
    print(f"✅ Phase 1 complete: {completed} markdown files downloaded, {failed} failed")

def _download_images_robust(works_dir: Path, work_dirs: set, files: list, progress: dict, progress_file: Path) -> None:
    """Download images with retry logic, progress persistence, and smaller batches"""
    import concurrent.futures
    import time
    import json
    from requests.exceptions import ReadTimeout, ConnectionError, HTTPError
    
    def download_work_images_with_retry(work_id: str, max_retries: int = 2) -> tuple:
        """Download all images for a single work with retry logic"""
        try:
            work_dir = works_dir / work_id
            images_dir = work_dir / "images"
            images_dir.mkdir(exist_ok=True)
            
            # Get list of image files for this work
            work_files = [f for f in files if f.startswith(f"works/{work_id}/images/")]
            print(f"🔍 Work {work_id}: Found {len(work_files)} image files to download")
            
            downloaded = 0
            failed = 0
            
            for img_file in work_files:
                img_name = img_file.split("/")[-1]
                local_path = images_dir / img_name
                
                # Skip if already downloaded
                if local_path.exists():
                    downloaded += 1
                    continue
                
                for attempt in range(max_retries):
                    try:
                        downloaded_file = hf_hub_download(
                            repo_id=ARTEFACT_MARKDOWN_DATASET,
                            filename=img_file,
                            repo_type="dataset"
                        )
                        
                        import shutil
                        shutil.copy2(downloaded_file, local_path)
                        downloaded += 1
                        break  # Success, exit retry loop
                        
                    except (ReadTimeout, ConnectionError, HTTPError) as e:
                        if attempt < max_retries - 1:
                            wait_time = 1 + attempt  # Short backoff for images
                            time.sleep(wait_time)
                        else:
                            failed += 1
                            if failed <= 5:  # Only print first few errors
                                print(f"⚠️  Could not download image {img_file}: {e}")
                    except Exception as e:
                        failed += 1
                        if failed <= 5:
                            print(f"⚠️  Unexpected error downloading {img_file}: {e}")
                        break  # Don't retry on unexpected errors
            
            return (work_id, downloaded, failed)
            
        except Exception as e:
            print(f"❌ Error downloading images for {work_id}: {e}")
            return (work_id, 0, 1)
    
    def save_progress():
        """Save current progress to file"""
        try:
            progress_data = {
                "markdown_completed": list(progress["markdown_completed"]),
                "image_batches_completed": list(progress["image_batches_completed"])
            }
            with open(progress_file, 'w') as f:
                json.dump(progress_data, f)
        except Exception as e:
            print(f"⚠️  Could not save progress: {e}")
    
    # Process works in much smaller batches to avoid resets
    work_list = list(work_dirs)
    batch_size = 5  # Much smaller batches - reduced from 20
    total_downloaded = 0
    total_failed = 0
    
    for i in range(0, len(work_list), batch_size):
        batch = work_list[i:i + batch_size]
        batch_id = f"batch_{i//batch_size + 1}"
        
        # Skip if batch already completed
        if batch_id in progress["image_batches_completed"]:
            print(f"⏭️  Skipping already completed batch {batch_id}")
            continue
        
        print(f"🖼️  Processing image batch {i//batch_size + 1}/{(len(work_list) + batch_size - 1)//batch_size} ({len(batch)} works)")
        
        with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:  # Single worker for images
            future_to_work = {executor.submit(download_work_images_with_retry, work_id): work_id for work_id in batch}
            
            for future in concurrent.futures.as_completed(future_to_work):
                work_id = future_to_work[future]
                try:
                    work_id, downloaded, failed = future.result()
                    total_downloaded += downloaded
                    total_failed += failed
                except Exception as e:
                    print(f"❌ Error processing {work_id}: {e}")
                    total_failed += 1
        
        # Mark batch as completed
        progress["image_batches_completed"].add(batch_id)
        save_progress()
        
        # Longer delay between batches to be very gentle on HF servers
        print(f"⏳ Waiting 10 seconds before next batch...")
        time.sleep(10)
    
    print(f"✅ Phase 2 complete: {total_downloaded} images downloaded, {total_failed} failed")

def _download_markdown_files_fallback(cache_dir: Path) -> Optional[Path]:
    """Fallback method to download markdown files individually"""
    try:
        works_dir = cache_dir / "works"
        works_dir.mkdir(exist_ok=True)
        
        # This is a simplified fallback - you might need to implement
        # a more sophisticated file discovery mechanism
        print("⚠️  Using fallback markdown loading - some files may be missing")
        return works_dir
        
    except Exception as e:
        print(f"❌ Fallback markdown loading failed: {e}")
        return None

def get_markdown_dir(force_refresh: bool = False) -> Path:
    """Get the markdown directory, loading from HF if needed"""
    global _markdown_dir_cache
    
    if _markdown_dir_cache is None or force_refresh:
        _markdown_dir_cache = load_markdown_dataset(force_refresh=force_refresh)
    
    if _markdown_dir_cache and _markdown_dir_cache.exists():
        return _markdown_dir_cache
    else:
        # Fallback to local directory if HF loading fails
        print("⚠️  Using fallback local markdown directory")
        return DATA_READ_ROOT / "marker_output"

# Legacy compatibility
JSON_DATASETS = load_json_datasets()
EMBEDDINGS_DATASETS = load_embeddings_datasets